Generalizing event studies using synthetic controls: An application to the Dollar Tree–Family Dollar acquisition

Abstract

Event studies, which have significantly advanced mergers and acquisitions (M&A) research, obtain excess returns based on a theory linking a firm's shareholder returns to those of the market. For outcomes lacking such a theory, we propose an empirical approach using a synthetic control method with machine learning to link outcomes for the acquirer or target to those for a group of comparison firms. We discuss the method's assumptions, its close parallel to event studies, and its difference in weighting comparison firms (based on data versus derived from theory). We provide an illustration of Dollar Tree's acquisition of Family Dollar, by analyzing shareholder returns (to demonstrate consistent results with an event study), realized cost and sales synergies, and customer sentiment (derived from more than 52 million Twitter messages). We highlight this method's potential—for M&A and other areas of strategy research—to open up new lines of inquiry.